Apparatus and methods for thermal management of electronic user devices

ABSTRACT

Apparatus and methods for thermal management of electronic user devices are disclosed herein. An example electronic device disclosed herein includes a housing, a fan, a first sensor, a second sensor, and a processor to at least one of analyze first sensor data generated by the first sensor to detect a presence of a subject proximate to the electronic device or analyze second sensor data generated by the second sensor to detect a gesture of the subject, and adjust one or more of an acoustic noise level generated the fan or a temperature of an exterior surface of the housing based on one or more of the presence of the subject or the gesture.

FIELD OF THE DISCLOSURE

This disclosure relates generally to electronic user devices and, moreparticularly, to apparatus and methods for thermal management ofelectronic user devices.

BACKGROUND

During operation of an electronic user device (e.g., a laptop, atablet), hardware components of the device, such as a processor, agraphics card, and/or battery, generate heat. Electronic user devicesinclude one or more fans to promote airflow to cool the device duringuse and prevent overheating of the hardware components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system constructed in accordance withteachings of this disclosure and including an example user device and anexample thermal constraint manager for controlling a thermal constraintof the user device.

FIG. 2 is a block diagram of an example implementation of the thermalconstraint manager of FIG. 1.

FIG. 3 illustrates example thermal constraints that may be implementedwith the example user device of FIG. 1.

FIG. 4 illustrates an example user device constructed in accordance withteachings of this disclosure and, in particular, illustrates the userdevice in a first configuration associated with a first thermalconstraint of the user device.

FIG. 5 illustrates the example user device of FIG. 4 and, in particular,illustrates the user device in a second configuration associated with asecond thermal constraint of the user device.

FIG. 6 is a flowchart representative of example machine readableinstructions which may be executed to implement the example trainingmanager of FIG. 2.

FIGS. 7A and 7B are flowcharts representative of example machinereadable instructions which may be executed to implement the examplethermal constraint manager of FIGS. 1 and/or 2.

FIG. 8 is a block diagram of an example processing platform structuredto execute the instructions of FIG. 6 to implement the example trainingmanager of FIG. 2.

FIG. 9 is a block diagram of an example processing platform structuredto execute the instructions of FIGS. 7A and 7B to implement the examplethermal constraint manager of FIGS. 1 and/or 2.

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

Descriptors “first,” “second,” “third,” etc. are used herein whenidentifying multiple elements or components which may be referred toseparately. Unless otherwise specified or understood based on theircontext of use, such descriptors are not intended to impute any meaningof priority, physical order or arrangement in a list, or ordering intime but are merely used as labels for referring to multiple elements orcomponents separately for ease of understanding the disclosed examples.In some examples, the descriptor “first” may be used to refer to anelement in the detailed description, while the same element may bereferred to in a claim with a different descriptor such as “second” or“third.” In such instances, it should be understood that suchdescriptors are used merely for ease of referencing multiple elements orcomponents.

DETAILED DESCRIPTION

During operation of an electronic user device (e.g., a laptop, atablet), hardware components disposed in a body or housing of thedevice, such as a processor, graphics card, and/or battery, generateheat. Heat generated by the hardware components of the user device cancause a temperature of one or more portions of an exterior surface, orskin, of the device housing to increase and become warm or hot to auser's touch. To prevent overheating of the hardware components, damageto the device, and/or discomfort to the user of the device when the usertouches or places one or more portions of the user's body proximate tothe skin of the device and/or components of the device accessible viathe exterior surface of the housing such as a touchpad, the user deviceincludes one or more fans to exhaust hot air generated within the bodyof the device and cool the device.

Some known electronic user devices are configured with one or morethermal constraints to control the temperature of the hardwarecomponents of the user device and/or of the skin of the device. Thethermal constraints(s) can define, for instance, a maximum temperatureof a hardware component such as a processor to prevent overheating ofthe processor. The thermal constraint(s) can define a maximumtemperature of the skin of the device to prevent discomfort to a usertouching and/or holding the device. In known user devices, operation ofthe fan(s) of the user device and/or management of power consumed by thedevice are controlled based on the thermal constraint(s). For instance,if a temperature of a hardware component of the device is approaching amaximum temperature as defined by the thermal constraint for thecomponent, rotational speed(s) (e.g., revolutions per minute (RPMs)) ofthe fan(s) can be increased to exhaust hot air and reduce a temperatureof the component. Additionally or alternatively, power consumption byone or more components of the device (e.g., the graphics card) may bereduced to reduce the amount of heat generated by the component and,thus, the device.

In some known user devices, the thermal constraint(s) define that atemperature of the skin of the device should not exceed, for instance,45° C., to prevent user discomfort when the user is physically touchingthe device (e.g., typing on a keyboard of a laptop, scrolling on atouchscreen, etc.). Temperature of the skin of the device can becontrolled by controlling power consumption of the hardware component(s)disposed within the device body to manage the amount of heat generatedby the component(s) transferred to the skin of the device. However, suchthermal constraint(s) can affect performance of the user device. Forinstance, some known user devices can operate in a high performancemode, or a mode that favors increased processing speeds over energyconservation (e.g., a mode in which processing speeds remain high forthe duration that the device is in use, the screen remains brightly lit,and other hardware components do not enter power-saving mode when thosecomponents are not in use). The processor consumes increased power toaccommodate the increased processing speeds associated with the highperformance mode and, thus, the amount of heat generated by theprocessor is increased. As a result, a temperature of the skin of theuser device can increase due to the increased amount of heat generatedwithin the device housing. In some known devices, the processor mayoperate at lower performance speeds to consume less power and, thus,prevent the skin of the device from exceeding the maximum skintemperature defined by the thermal constraint. Thus, in some knowndevices, processing performance is sacrificed in view of thermalconstraint(s).

Higher fan speeds can be used to facilitate of cooling of hardwarecomponent(s) of a device to enable the component(s) to operate in, forinstance, a high performance mode without exceeding the thermalconstraint(s) for the hardware competent(s) and/or the device skin.However, operation of the fan(s) at higher speeds increases audibleacoustic noise generated by the fan(s). Thus, in some known userdevices, the fan speed(s) and, thus, the amount of cooling that isprovided by the fan(s), are restricted to avoid generating fan noiselevels over certain decibels. Some know devices define fan noiseconstraints that set, for instance, a maximum noise level of 35 dBAduring operation of the fan(s). As a result of the restricted fanspeed(s), performance of the device may be limited to enable the fan(s)to cool the user device within the constraints of the fan speed(s).

In some instances, cooling capabilities of the fan(s) of the devicedegrade over time due to dust accumulating in the fan(s) and/or heatsink. Some known user devices direct the fan(s) to reverse airflowdirection (e.g., as compared to the default airflow direction to exhausthot air from the device) to facilitate heatsink and fan shroud cleaning,which helps to de-clog dust from the airflow path and maintain deviceperformance over time. However, operation of the fan(s) in the reversedirection increases audible acoustics generated by the fan(s), which candisrupt the user's experience with the device.

Although thermal constraint(s) are implemented in a user device toprevent discomfort to the user when the user is directly touching thedevice (e.g., physically touching one or more components of the deviceaccessible via the exterior housing of the device, such a keyboardand/or touchpad of a laptop, a touchscreen of a tablet, etc.), there areinstances in which a temperature of the skin of the device can beincreased without affecting the user's experience with the device. Forinstance, a user may view a video on the user device but not physicallytouch the user device; rather, the device may be resting on a table. Insome instances, the user may interact with the user device via externalaccessories communicatively coupled to the device, such as an externalkeyboard and/or an external mouse. In such instances, because the useris not directly touching the device (i.e., not directly touching theskin of the device housing and/or component(s) accessible via theexterior surface of the housing), an increase in a temperature of theskin of the device would not be detected by the user. However, knownuser devices maintain the skin temperature of the device at the sametemperature as if the user were directly touching the user deviceregardless of whether the user is interacting with the device viaexternal accessories.

In some instances, the user device is located in a noisy environment(e.g., a coffee shop, a train station). Additionally, or alternatively,in some instances, the user may be interacting with the user devicewhile wearing headphones. In such instances, the amount of fan noiseheard by the user is reduced because of the loud environment and/or theuse of headphones. However, in known user devices, the rotational speedof the fan(s) of the device are maintained at a level that minimizesnoise from the fan(s) regardless of the surrounding ambient noise levelsand/or whether or not the user is wearing headphones.

Disclosed herein are example user devices that provide for dynamicadjustment of thermal constraints and/or fan acoustic noise levels ofthe user device. Example disclosed herein use a multi-tier determinationto control operation of fan(s) of the device and/or to adjust aperformance level of the device and, thus, control heat generated byhardware component(s) of the device based on factors such as a presenceof a user proximate to the device, user interaction(s) with the device(e.g., whether the user is using an on-board keyboard of the device oran external keyboard), and/or ambient noise levels in an environment inwhich the device is located. Example user devices disclosed hereininclude sensors to detect user presence (e.g., proximity sensor(s),image sensor(s)), device configuration (e.g., sensor(s) to detect userinput(s) received via an external keyboard, sensor(s) to detect deviceorientation), and/or conditions in the ambient environment in which thedevice is located (e.g., ambient noise sensor(s)). Based on the sensordata, examples disclosed herein determine whether a temperature of theskin of the device housing can be increased relative to a defaultthermal constraint, where the default thermal constraint corresponds toa skin temperature for the device when the user is directly touching thedevice (e.g., touching one or more components of the device accessiblevia the exterior housing of the device such as keyboard or touchpad of alaptop). Examples disclosed herein selectively control an amount ofpower provided to hardware component(s) of the user device and/or fanspeed level(s) (e.g., RPMs) based on the selected thermal constraint(e.g., the default thermal constraint or a thermal constraint permittinga higher skin temperature for the device relative to the default thermalconstraint).

In some examples disclosed herein, power consumption by one or morecomponent(s) of the user device (e.g., the processor) is increased whenthe user is determined to be providing inputs to the user device via,for instance, an external keyboard. Because the user is not physicallytouching the exterior surface of the device housing when the user isproviding inputs via the external keyboard, the temperature of the skinof the device can be increased without adversely affecting the user(e.g., without causing discomfort to the user). In some examplesdisclosed herein, rotational speed(s) (e.g. RPM(s)) of the fan(s) of theuser device are increased when sensor data from the ambient noisesensor(s) indicates that the user is in a loud environment. In suchexamples, because the user device is located in a noisy environment, theresulting increase in fan acoustics from the increased rotationalspeed(s) of the fan(s) is offset by the ambient noise. In some otherexamples, the rotational direction of the fan(s) of the user device isreversed (e.g., to facilitate heatsink and fan shroud cleaning) whensensor data from the ambient noise sensor(s) indicate that the userdevice is in a loud environment and/or is that the user is not presentor within a threshold distance of the device. Thus, the user is notinterrupted by the increased fan noise and the device can be cooledand/or cleaned with increased efficiency. Rather than maintaining thethermal constraint(s) of the device and/or the fan noise constraint(s)at respective default levels during operation of the device, examplesdisclosed herein dynamically adjust the constraints and, thus, theperformance of the device, based on user and/or environmental factors.As a result, performance of the device can be selectively increased inview of the opportunities for increased device skin temperature and/oraudible fan noise levels in response to user interactions with thedevice.

FIG. 1 illustrates an example system 100 constructed in accordance withteachings of this disclosure for controlling thermal constraint(s)and/or fan noise constraint(s) for a user device 102. The user device102 can be, for example, a personal computing (PC) device such as alaptop, a desktop, an electronic tablet, a hybrid or convertible PC,etc. In some examples, the user device 102 includes a keyboard 104. Inother examples, such as when the user device 102 is an electronictablet, a keyboard is presented via a display screen 103 of the userdevice 102 and the user provides inputs on the keyboard by touching thescreen. In some examples, the user device 102 includes one or morepointing device(s) 106 such as a touchpad. In examples disclosed herein,the keyboard 104 and the pointing device(s) 106 are carried by a housingthe user device 102 and accessible via an exterior surface of thehousing and, thus, can be considered on-board user input devices for thedevice 102.

In some examples, the user device 102 additionally or alternativelyincludes one or more external devices communicatively coupled to thedevice 102, such as an external keyboard 108, external pointingdevice(s) 110 (e.g., wired or wireless mouse(s)), and/or headphones 112.The external keyboard 108, the external pointing device(s) 110, and/orthe headphones 112 can be communicatively coupled to the user device 102via one or more wired or wireless connections. In the example of FIG. 1,the user device 102 includes one or more device configuration sensor(s)120 that provide means for detecting whether user input(s) are beingreceived via the external keyboard 108 and/or the external pointingdevice(s) 110 and/or whether output(s) (e.g., audio output(s)) are beingdelivered via the headphones 112 are coupled to the user device 102. Insome examples, the device status sensor(s) 120 detect a wired connectionof one or more of the external devices 108, 110, 112 via a hardwareinterface (e.g., USB port, etc.). In other examples, the deviceconfiguration sensor(s) 120 detect the presence of the externaldevice(s) 108, 110, 112 via wireless connection(s) (e.g., Bluetooth). Insome examples, the device configuration sensor(s) 120 includeaccelerometers to detect an orientation of the device 102 (e.g., tabletmode) and/or sensor(s) to detect an angle of, for instance, a screen ofa laptop (e.g., facing the laptop base, angled away from the base,etc.).

The example user device 102 includes a processor 130 that executessoftware to interpret and output response(s) based on the user inputevent(s) (e.g., touch event(s), keyboard input(s), etc.). The userdevice 102 of FIG. 1 includes one or more power sources 116 such as abattery to provide power to the processor 130 and/or other components ofthe user device 102 communicatively coupled via a bus 117.

In the example of FIG. 1, the hardware components of the device 102(e.g., the processor 130, a video graphics card, etc.) generate heatduring operation of the user device 102. The example user device 102includes temperature sensor(s) 126 to measure temperature(s) associatedwith the hardware component(s) of the user device 102. In the example ofFIG. 1, the temperature sensor(s) 126 measure a temperature of a skin ofthe housing of the user device 102, or an exterior surface of the userdevice that can be touched by a user (e.g., a base of a laptop) (theterms “user” and “subject” are used interchangeably herein and bothrefer to a biological creature such as a human being). The temperaturesensor(s) 126 can be disposed in the housing of the device 102 proximateto the skin (e.g., coupled to a side of the housing opposite the side ofthe housing that is visible to the user). The temperature sensor(s) 126can include one or more thermometers.

The example user device 102 of FIG. 1 includes one or more fan(s) 114.The fan(s) 114 provide means for cooling and/or regulating thetemperature of the hardware component(s) (e.g., the processor 130) ofthe user device 102 in response to temperature data generated by thetemperature sensor(s) 126. In the example of FIG. 1, operation of thefan(s) 114 is controlled in view of one or more thermal constraints forthe user device 102 that define temperature settings for the hardwarecomponent(s) of the device 102 and/or a skin temperature of the device102. In some examples, operation of the fan(s) 114 of the example userdevice 102 of FIG. 1 is controlled based on one or more fan acousticconstraints that define noise level(s) (e.g., decibels) to be generatedduring operation of the fan(s) 114. In the example of FIG. 1, thethermal constraint(s) and/or fan acoustic constraint(s) for the device102 are dynamically selected based on the user interaction(s) with thedevice 102 and/or ambient conditions in an environment in which thedevice 102 is located.

The example user device 102 of FIG. 1 includes one or more user presencedetection sensor(s) 118. The user presence detection sensor(s) 118provide a means for detecting a presence of a user relative to the userdevice 102 in an environment in which the user device 102 is located.For example, the user presence detection sensor(s) 118 may detect a userapproaching the user device 102. In the example of FIG. 1, the userpresence detection sensor(s) 118 include proximity sensor(s) that emitelectromagnetic radiation (e.g., light pulses) and detect changes in thesignal due to the presence of a person or object (e.g., based onreflection of the electromagnetic radiation (e.g., light pulses). Insome examples, the user presence detection sensor(s) 118 includetime-of-flight (TOF) sensors that measure a length of time for light toreturn to the sensor after being reflected off a person or object, whichcan be used to determine depth. The example user presence detectionsensor(s) 118 can include other types of depth sensors, such as sensorsthat detect changes based on radar or sonar data. In some instances, theuser presence detection sensor(s) 118 collect distance measurements forone or more (e.g., four) spatial regions (e.g., non-overlappingquadrants) relative to the user device 102. The user presence detectionsensor(s) 118 associated with each region provide distance range datafor region(s) of the user's face and/or body corresponding to theregions.

The user presence detection sensor(s) 118 are carried by the exampleuser device 102 such that the user presence detection sensor(s) 118 candetect changes in an environment in which the user device 102 is locatedthat occur with a range (e.g., a distance range) of the user presencedetection sensor(s) 118 (e.g., within 10 feet of the user presencedetection sensor(s) 118, within 5 feet, etc.). For example, the userpresence detection sensor(s) 118 can be mounted on a bezel of thedisplay screen 103 and oriented such that the user presence detectionsensor(s) 118 can detect a user approaching the user device 102. Theuser presence detection sensor(s) 118 can additionally or alternativelybe at any other locations on the user device 102 where the sensor(s) 118face an environment in which the user device 102 is located, such as ona base of the laptop (e.g., on an edge of the base in front of akeyboard carried by base), a lid of the laptop, on a base of the laptopsupporting the display screen 103 in examples where the display screen103 is a monitor of a desktop or all-in-one PC, etc.

In some examples, the user presence detection sensor(s) 118 areadditionally or alternatively mounted at locations on the user device102 where the user's arm, hand, and/or finger(s) are likely to move orpass over as the user brings his or her arm, hand, and/or finger(s)toward the display screen 103, the keyboard 104, and/or other user inputdevice (e.g., the pointing device(s) 106). For instance, in examples inwhich the user device 102 is laptop or other device including atouchpad, the user presence detection sensor(s) 118 can be disposedproximate to the touchpad of the device 102 to detect when a user's armis hovering over the touchpad (e.g., as the user reaches for the screen103 or the keyboard 104).

In the example of FIG. 1, the user device 102 includes image sensor(s)122. In this example, the image sensor(s) 122 generate image data thatis analyzed to detect, for example, a presence of the user proximate tothe device, gestures performed by the user, whether the user is lookingtoward or away from the display screen 103 of the device 102 (e.g.,eye-tracking), etc. The image sensor(s) 122 of the user device 102include one or more cameras to capture image data of the surroundingenvironment in which the device 102 is located. In some examples, theimage sensor(s) 122 include depth-sensing camera(s). In the example ofFIG. 1, the image sensor(s) 122 are carried by the example user device102 such that when a user faces the display screen 103, the user iswithin a field of view of the image sensor(s) 122. For example, theimage sensor(s) 122 can be carried by a bezel of the display screen 103.

The example user device 102 of FIG. 1 includes one or more motionsensor(s) 123. The motion sensor(s) 123 can include, for example,infrared sensor(s) to detect user movements. As disclosed herein, datagenerated by the motion sensor(s) 123 can be analyzed to identifygestures performed by the user of the user device 102. The motionsensor(s) 123 can be carried by the device 102 proximate to, forexample, a touchpad of the device 102, a bezel of the display screen103, etc. so as to detect user motion(s) occurring proximate to thedevice 102.

In the example of FIG. 1, the user device 102 includes one or moremicrophone(s) 124 to detect sounds in an environment in which the userdevice 102 is located. The microphone(s) 124 can be carried by the userdevice 102 at one or more locations, such as on a lid of the device 102,on a base of the device 102 proximate to the keyboard 104, etc.

The example user device 102 of FIG. 1 can include other types ofsensor(s) to detect user interactions relative to the device 102 and/orenvironmental conditions (e.g., ambient light sensor(s)).

The example user device 102 includes one or more semiconductor-basedprocessors to process sensor data generated by the user presencedetection sensor(s) 118, the device configuration sensor(s) 120, theimage sensor(s) 122, the motion sensor(s) 123, the microphone(s) 124,and/or the temperature sensor(s) 126. For example, the sensor(s) 118,120, 122, 123, 124, 126 can transmit data to the on-board processor 130of the user device 102. In other examples, the sensor(s) 118, 120, 122,123, 124, 126 can transmit data to a processor 127 of another userdevice 128, such as such as a smartphone or a wearable device such as asmartwatch. In other examples, the sensor(s) 118, 120, 122, 123, 124,126 can transmit data to a cloud-based device 129 (e.g., one or moreserver(s), processor(s), and/or virtual machine(s)).

In some examples, the processor 130 of the user device 102 iscommunicatively coupled to one or more other processors. In such anexample, the sensor(s) 118, 120, 122, 123, 124, 126 can transmit thesensor data to the on-board processor 130 of the user device 102. Theon-board processor 130 of the user device 102 can then transmit thesensor data to the processor 127 of the user device 128 and/or thecloud-based device(s) 129. In some such examples, the user device 102(e.g., the sensor(s) 118, 120, 122, 123, 124, 126 and/or the on-boardprocessor 130) and the processor(s) 127, 130 are communicatively coupledvia one or more wired connections (e.g., a cable) or wirelessconnections (e.g., cellular, Wi-Fi, or Bluetooth connections). In otherexamples, the sensor data may only be processed by the on-boardprocessor 130 (i.e., not sent off the device).

In the example system 100 of FIG. 1, the sensor data generated by theuser presence detection sensor(s) 118, the device configurationsensor(s) 120, the image sensor(s) 122, the motion sensor(s) 123, themicrophone(s) 124, and/or the temperature sensor(s) 126 is processed bya thermal constraint manager 132 to select a thermal constraint for theuser device 102 to affect a temperature of the skin of the housing ofthe device 102 and/or a fan acoustic constraint to affect rotationalspeed(s) of the fan(s) 114 of the user device 102 and, thus, noisegenerated by the fan(s) 114. As a result of the selected thermalconstraint and/or fan acoustic constraint, the example thermalconstraint manager 132 can affect performance of the device 102. Forinstance, if the thermal constraint manager 132 determines that thetemperature of the skin of the device 102 can be increased and/or thatrotational speed(s) of the fan(s) 114 can be increased, additional powercan be provided to hardware component(s) of the device 102 (e.g., theprocessor 130) to provide for increased performance of the component(s)(e.g., higher processing speeds). In such examples, the increased heatgenerated by the hardware component(s) and transferred to the skin ofthe device is permitted by the selected thermal constraint and/or ismanaged via increased rotation of the fan(s) 114. In the example of FIG.1, the thermal constraint manager 132 is implemented by executableinstructions executed on the processor 130 of the user device 102.However, in other examples, the thermal constraint manager 132 isimplemented by instructions executed on the processor 127 of thewearable or non-wearable user device 128 and/or on the cloud-baseddevice(s) 129. In other examples, the thermal constraint manager 132 isimplemented by dedicated circuitry located on the user device 102 and/orthe user device 128. These components may be implemented in software,firmware, hardware, or in combination of two or more of software,firmware, and hardware.

In the example of FIG. 1, the thermal constraint manager 132 serves toprocess the sensor data generated by the respective sensor(s) 118, 120,122, 123, 124, 126 to identify user interaction(s) with the user device102 and/or ambient conditions in the environment in which the device 102is located and to select a thermal constraint and/or fan acousticconstraint for the user device 102 based on the user interaction(s)and/or the ambient environment conditions. In some examples, the thermalconstraint manager 132 receives the sensor data in substantiallyreal-time (e.g., near the time the data is collected). In otherexamples, the thermal constraint manager 132 receives the sensor data ata later time (e.g., periodically and/or aperiodically based on one ormore settings but sometime after the activity that caused the sensordata to be generated, such as a hand motion, has occurred (e.g.,seconds, minutes, etc. later)). The thermal constraint manager 132 canperform one or more operations on the sensor data such as filtering theraw signal data, removing noise from the signal data, converting thesignal data from analog data to digital data, and/or analyzing the data.For example, the thermal constraint manager 132 can convert the sensordata from analog to digital data at the on-board processor 130 and thedigital data can be analyzed by on-board processor 130 and/or by one ormore off-board processors, such as the processor 127 of the user device128 and/or the cloud-based device 129.

Based on the sensor data generated by the user presence detectionsensor(s) 118, the thermal constraint manager 132 determines whether ornot a subject is present within the range of the user presence detectionsensor(s) 118. In some examples, if the thermal constraint manager 132determines that the user is not within the range of the user presencedetection sensor(s) 118, the thermal constraint manager 132 determinesthat the rotational speed of the fan(s) 114 can be increased, as theuser is not present to hear the increased acoustic noise generated bythe fan(s) 114 operating at an increased speed. The thermal constraintmanager 132 generates instructs for the fan(s) 114 to increase therotational speed at which the fan(s) 114 operate. The fan(s) 114 cancontinue to operate at the increased rotational speed to provideefficient until, for instance, the processor 130 of the device 102determines that no user input(s) have been received at the device 102for a period of time and the device 102 should enter a low power state(e.g., a standby or sleep state).

In the example of FIG. 1, if the thermal constraint manager 132determines that a user is within the range of the user presencedetection sensor(s) 118, the thermal constraint manager 132 determinesif the user is interacting with the device 102. The thermal constraintmanager 132 can detect whether user input(s) are being received via (a)the on-board keyboard 104 and/or the on-board pointing device(s) 106 or(b) the external keyboard 108 and/or the external pointing device(s) 110based on data generated by the device configuration sensor(s) 120. Ifthe user is interacting with the device 102 via the on-board keyboard104 and/or the on-board pointing device(s) 106, the thermal constraintmanager 132 maintains the skin temperature of the device 102 at a first(e.g., default) thermal constraint that defines a maximum temperaturefor the device skin to prevent the skin of the device housing frombecoming too hot and injuring the user. If the thermal constraintmanager 132 determines that the user is interacting with the device 102via the external keyboard 108 and/or the external pointing device(s)110, the thermal constraint manager 132 selects a thermal constraint forthe device that defines an increased temperature for the skin of thedevice 102 relative to the first thermal constraint. As a result of therelaxation of the thermal constraint for the device 102 (i.e., thepermitted increase in the skin temperature of the device), one or morehardware component(s) of the device 102 (e.g., the processor 130) moveto an increased performance mode in which the component(s) of the deviceconsume more power and, thus, generate more heat. In such examples, thethermal constraint manager 132 selects a thermal constraint for the skintemperature of the device housing that is increased relative to thethermal constraint selected when the user is interacting with the device102 via the on-board keyboard 104 and/or the on-board pointing device(s)106 because the user is not directly touching the device 102 whenproviding input(s) via the external device(s) 108, 110.

If the thermal constraint manager 132 determines that the user is withinthe range of the user presence detection sensor(s) 118 but is notproviding input(s) at the device 102 and/or has not provided an inputwithin a threshold period of time, the thermal constraint manager 132infers a user intent to interact with the device. The thermal constraintmanager 132 can use data from multiple types of sensors to predictwhether the user is likely to interact with the device.

For example, the thermal constraint manager 132 can determine a distanceof the user from the device 102 based on data generated by the userpresence detection sensor(s) 118. If the user is determined to beoutside of a predefined threshold range of the device 102 (e.g., fartherthan 1 meter from the device 102), the thermal constraint manager 132determines that the rotational speed of the fan(s) 114 of the device 102and, thus, the fan acoustics, can be increased because the increased fannoise will not disrupt the user in view of the user's distance from thedevice 102. Additionally or alternatively, the thermal constraintmanager 132 determines that the power level of the power source(s) 116of the device 102 and, thus, the device skin temperature, can beincreased because the increased skin temperature will not causediscomfort to the user based on the user's distance from the device 102.

In some examples, thermal constraint manager 132 analyzes image datagenerated by the image sensor(s) 122 to determine a position of theuser's eyes relative to the display screen 103 of the device 102. Insuch examples, if thermal constraint manager 132 identifies both of theuser's eyes in the image data, the thermal constraint manager 132determines that the user is looking at the display screen 103. If thethermal constraint manager 132 identifies one of the user's eyes or noneof the user's eyes in the image data, the thermal constraint manager 132determines that the user is not engaged with the device 102. In suchexamples, the thermal constraint manager 132 can instruct the fan(s) 114to increase rotational speed(s) to cool the device 102. Because the useris not engaged or not likely engaged with the device 102 as determinedbased on eye tracking, the thermal constraint manager 132 permitsincreased fan noise to be generated by the fan(s) 114 to efficientlycool the device 102 while the user is distracted relative to the device102. Additionally or alternatively, the thermal constraint manager 132can instruct the power source(s) 116 to increase the power provided tothe hardware component(s) of the user device 102 (and, thus, resultingin increased the skin temperature of the user device 102).

In some examples, the thermal constraint manager 132 analyzes the imagedata generated by the image data sensor(s) 122 and/or the motionsensor(s) 123 to identify gesture(s) being performed by the user. If thethermal constraint manager 132 determines that the user is, forinstance, looking away from the device 102 and talking on the phonebased on the image data and/or the motion sensor data (e.g. image dataand/or motion sensor data indicating that the user has moved his or herhand proximate to his or her ear), the thermal constraint manager 132determines that the fan acoustics can be increased because the user isnot likely to interact with the device 102 while the user is lookingaway and talking on the phone.

The example thermal constraint manager 132 of FIG. 1 evaluates ambientnoise conditions to determine if fan noise levels can be increased. Thethermal constraint manager 132 of FIG. 1 analyzes data generated by themicrophone(s) 124 to determine if ambient noise in the surroundingenvironment exceeds an environment noise level threshold. If the thermalconstraint manager 132 determines that the ambient noise exceeds theenvironment noise level threshold, the thermal constraint manager 132instructs the fan(s) to rotate at increased speed(s) and, thus, generateincreased fan noise. In such examples, the increased fan noise isunlikely to be detected in the noisy environment in which the userdevice 102 is located and, thus, operation of the fan(s) 114 can beoptimized to increase cooling and, thus, performance of the device 102.

Additionally or alternatively, the thermal constraint manager 132 candetermine whether the user is wearing headphones based on, for example,image data generated by the image sensor(s) 122 and/or data from thedevice configuration sensor(s) 120 indicating that headphones areconnected to the device 102 (e.g., via wired or wireless connection(s)).In such examples, the thermal constraint manager 132 instructs thefan(s) 114 to rotate at increased speed(s) to increase cooling of thedevice 102 because the resulting increased fan noise is unlikely to bedetected by the user who is wearing headphones.

The thermal constraint manager 132 dynamically adjusts the thermalconstraint(s) and/or fan noise levels for the device 102 based on theinferred user intent to interact with the device and/or conditions inthe environment. In some examples, the thermal constraint manager 132determines that the user likely to interact with the device afterpreviously instructing the fan(s) to increase rotational speed(s) basedon, for example, data from the user presence detection sensor(s) 118indicating that the user is moving toward the device 102 and/or reachingfor the on-board keyboard. In such examples, the thermal constraintmanager 132 instructs the fan(s) 114 to reduce the rotation speed and,thus, the fan noise in view of the expectation that the user is going tointeract with the device 102.

As another example, if the thermal constraint manager 132 determinesthat the user is providing input(s) via the external device(s) 108, 110and, thus, selects a thermal constraint for the device 102 thatincreases the temperature of the skin of the device. If, at later time,the thermal constraint manager 132 determines that the user is reachingfor the display screen 103 (e.g., based on data from the user presencedetection sensor(s) 118, the image sensor(s) 122, and/or the motionsensor(s) 123), the thermal constraint manager selects a thermalconstraint that results in decreased temperature of the device skin. Insuch examples, power consumption by the hardware component(s) of thedevice 102 and/or fan speed(s) can be adjusted to cool the device 102.

As another example, if the thermal constraint manager 132 determines ata later time that the user is no longer wearing the headphones 112(e.g., based on the image data) after previously determining that theuser was wearing the headphones 112, the thermal constraint manager 132instructs the fan(s) 114 to reduce rotational speed to generate lessnoise.

In some examples, the thermal constraint manager 132 dynamically adjuststhe thermal constraint(s) and/or fan acoustic constraint(s) based ontemperature data generated by the temperature sensor(s) 126. Forexample, if data from the temperature sensor(s) 126 indicates that skintemperature is approaching the threshold defined by a selected thermalconstraint, the thermal constraint manager 132 generates instructions tomaintain or reduce the skin temperature by adjusting power consumptionof the hardware component(s) and/or by operation of the fan(s) 114.

FIG. 2 is a block diagram of an example implementation of the thermalconstraint manager 132 of FIG. 1. As mentioned above, the thermalconstraint manager 132 is constructed to detect user interaction(s)and/or ambient condition(s) relative to the user device 102 and togenerate instructions that cause the user device 102 to transitionbetween one or more thermal constraints with respect to skin temperatureof the device 102 and/or one or more fan acoustic constraints withrespect to audible noise generated by the fan(s) 114 of the device 102.In the example of FIG. 2, the thermal constraint manager 132 isimplemented by one or more of the processor 130 of the user device 102,the processor 127 of the second user device 128, and/or cloud-baseddevice(s) 129 (e.g., server(s), processor(s), and/or virtual machine(s)in the cloud 129 of FIG. 1). In some examples, some of the userinteraction analysis and/or ambient condition analysis is implemented bythe thermal constraint manager 132 via a cloud-computing environment andone or more other parts of the analysis is implemented by the processor130 of the user device 102 being controlled and/or the processor 127 ofa second user device 128 such as a wearable device

As illustrated in FIG. 2, the example thermal constraint manager 132receives user presence sensor data 200 from the user presence detectionsensor(s) 118 of the example user device 102 of FIG. 1, deviceconfiguration sensor data 202 from the device configuration sensor(s)120, image sensor data 204 from the image sensor(s) 122, gesture data205 from the motion sensor(s) 123, ambient noise sensor data 206 fromthe microphone(s) 124, and temperature sensor data 208 from thetemperature sensor(s) 126. The sensor data 200, 202, 204, 205, 206, 208is stored in a database 212. In some examples, the thermal constraintmanager 132 includes the database 212. In other examples, the database212 is located external to the thermal constraint manager 132 in alocation accessible to the thermal constraint manager 132 as shown inFIG. 2.

The thermal constraint manager 132 includes a user presence detectionanalyzer 214. In this example, the user presence detection analyzer 214provides means for analyzing the sensor data 200 generated by the userpresence detection sensor(s) 118. In particular, the user presencedetection analyzer 214 analyzes the sensor data 200 to determine if auser is within the range of the user presence detection sensor(s) 118and, thus, is near enough to the user device 102 to suggest that theuser is about to use the user device 102. In some examples, the userpresence detection analyzer 214 determines if the user is within aparticular distance from the user device 102 (e.g., within 0.5 meters ofthe device 102, within 0.75 meters of the device 102). The user presencedetection analyzer 214 analyzes the sensor data 200 based on one or moreuser presence detection rule(s) 216. The user presence detection rule(s)216 can be defined based on user input(s) and stored in the database212.

The user presence detection rule(s) 216 can define, for instance,threshold time-of-flight (TOF) measurements by the user presencedetection sensor(s) 118 that indicate presence of the user is within arange from the user presence detection sensor(s) 118 (e.g., measurementsof the amount of time between emission of a wave pulse, reflection off asubject, and return to the sensor). In some examples, the user presencedetection rule(s) 216 define threshold distance(s) for determining thata subject is within proximity of the user device 102. In such examples,the user presence detection analyzer 214 determines the distance(s)based on the TOF measurement(s) in the sensor data 200 and the knownspeed of the light emitted by the sensor(s) 118. In some examples, theuser presence detection analyzer 214 identifies changes in the depth ordistance values over time and detects whether the user is approachingthe device 102 or moving away from the user device 102 based on thechanges. The threshold TOF measurement(s) and/or distance(s) for thesensor data 200 can be based on the range of the sensor(s) 118 inemitting pulses. In some examples, the threshold TOF measurement(s)and/or distances are based on user-defined reference distances fordetermining that a user is near or approaching the user device 102 ascompared to simply being in the environment in which the user device 102and the user are both present.

The example thermal constraint manager 132 of FIG. 2 includes a deviceconfiguration analyzer 218. In this example, the device configurationanalyzer 218 provides means for analyzing the sensor data 202 generatedby the device configuration sensor(s) 120. The device configurationanalyzer 218 analyzes the sensor data 202 to detect, for example,whether user input(s) are being received via the on-board keyboard 104and/or the on-board pointing device(s) 106 of the user device 102 or viaone or more external devices (e.g., the external keyboard 108, theexternal pointing device(s) 110) communicatively coupled to the userdevice 102. In some examples, the device configuration analyzer 218detects that audio output(s) from the device 102 are being delivered viaan external output device such as the headphones 112. In some examples,the device configuration analyzer 218 analyzes the orientation of thedevice 102 to infer, for example, whether a user is sitting whileinteracting with device 102, standing while interacting with the device102 (e.g., based on an angle of a display screen of the device 102),whether the device 102 is in tablet mode, etc.

The device configuration analyzer 218 analyzes the sensor data 202 basedon one or more device configuration rule(s) 219. The deviceconfiguration rule(s) 219 can be defined based on user input(s) andstored in the database 212. The device configuration rule(s) 219 candefine, for example, identifiers for recognizing when external device(s)such as the headphones 112 of FIG. 1 are communicatively coupled to theuser device 102 via one or more wired or wireless connections. Thedevice configuration rule(s) 219 define rule(s) for detecting userinput(s) being received at the user device via the external device(s)108, 110 based on data received from the external device(s). The deviceconfiguration rule(s) 219 define rule(s) for detecting audio output(s)delivered via the external device such as the headphone(s) 118. Thedevice configuration rule(s) 219 can define rule(s) indicating that ifthe display screen 103 is angled within a particular angle range (e.g.,over 90° relative to a base of laptop), the user is sitting whileinteracting with the device 102.

The example thermal constraint manager 132 of FIGS. 1 and 2 is trainedto recognize user interaction(s) relative to the user device 102 topredict whether the user is likely to interact with the device 102. Inthe example of FIG. 2, the thermal constraint manager 132 analyzes oneor more of the sensor data 204 from the image sensor(s) 122 and/or thesensor data 205 from the motion sensor(s) 123 to detect user activityrelative to the device 102. In the example of FIG. 2, the thermalconstraint manager 132 is trained to recognize user interactions by atraining manager 224 using machine learning and training sensor data forone or more subjects, which may or may not include sensor data generatedby the sensor(s) 122, 123 of the user device 102 of FIG. 1. In someexamples, the training sensor data is generated from subject(s) who areinteracting with the user device 102 and/or a different user device. Thetraining sensor data is stored in a database 232. In some examples, thetraining manager 224 includes the database 232. In other examples, thedatabase 232 is located external to the training manager 224 in alocation accessible to the training manager 224 as shown in FIG. 2. Thedatabases 212, 232 of FIG. 2 may be the same storage device or differentstorage devices.

In the example of FIG. 2, the training sensor data includes traininggesture data 230, or data including a plurality of gestures performed byuser(s) and associated user interactions represented by the gestures inthe context of interacting with the user device 102. For instance, thetraining gesture data 230 can include a first rule indicating that if auser raises his or her hand proximate to his or her ear, the user istalking on a telephone. The training gesture data 230 can include asecond rule indicating that if a user is reaching his or her hand awayfrom his or her body as detected by a motion sensor disposed proximateto a keyboard of the device and/or as captured in image data, the useris reaching for the display screen of the user device. The traininggesture data 230 can include a third rule indicating that if only aportion of the user's body from the waist upward is visible in imagedata, the user is in a sitting position.

In the example of FIG. 2, the training sensor data includes trainingfacial feature data 231, or data including a plurality of images ofsubject(s) and associated eye position data, mouth position data, headaccessory data (e.g., headphone usage) represented by the image(s) inthe context of viewing the display screen 103 of the device 102, lookingaway from the display screen 103 of the device 102, interacting with thedevice 102 while wearing headphones, etc. The training facial featuredata 231 can include a first rule that if both of the user's eyes arevisible in image data generated by the image sensor(s) 122 of the userdevice 102, then the user is looking at the display screen 103 of thedevice 102. The training facial feature data 231 can include a secondrule that if one of the user's eyes is visible in the image data, theuser is likely to interact with the device 102. The training facialfeature data 231 can include a third rule that if neither of the user'seyes is visible in the image data, the user is looking away from thedevice 102. The training facial feature data 231 can include a fourthrule that if the user's mouth is open in the image data, the user istalking. The training facial feature data 231 can include a fifth rulethat identifies when a user is wearing headphones based on feature(s)detected in the image data.

The example training manager 224 of FIG. 2 includes a trainer 226 and amachine learning engine 228. The trainer 226 trains the machine learningengine 228 using the training gesture data 230 and the training facialfeature data 231 (e.g., via supervised learning) to generate one or moremodel(s) that are used by the thermal constraint manager 132 to controlthermal constraints of the user device 102 based on user interaction(s)and/or inferred intent regarding user interaction(s) with the device102. For example, the trainer 226 uses the training gesture data 230 togenerate one or more gesture data models 223 via the machine learningengine 228 that define user interaction(s) relative to the device 102 inresponse to particular gestures performed by the user. As anotherexample, the trainer 226 users the training facial feature data 231 togenerate one or more facial feature data models 225 via the machinelearning engine 228 that that define user interaction(s) relative to thedevice 102 in response to particular eye tracking positions, facialexpressions of the user, and/or head accessories (e.g., headphones) wornby the user. In the example of FIG. 2, the gesture data model(s) 223 andthe facial feature data model(s) 225 are stored in the database 212. Theexample database 212 can store additional or fewer models than shown inFIG. 2. For example, the database 212 can store a model generated duringtraining based on the training gesture data 230 and data indicative of adistance of the user relative to the device (e.g., based on proximitysensor data) and/or device configuration (e.g., based on sensor dataindicating screen orientation).

The example thermal constraint manager 132 of FIG. 2 uses the model(s)223, 225 to interpret the respective sensor data generated by the motionsensor(s) 123 and/or the image sensor(s) 122. The example thermalconstraint manager 132 of FIG. 2 includes a motion data analyzer 222. Inthis example, the motion data analyzer 222 provides means for analyzingthe sensor data 205 generated by the motion sensor(s) 123, The examplemotion data analyzer 222 uses the gesture data model(s) 223 to identifygesture(s) performed by the user relative to the device 102. Forexample, based on the gesture data model(s) 223 and the sensor data 205generated by the motion sensor(s) 123 disposed proximate to, forinstance, display screen 103 of the device 102 and/or a touchpad of thedevice 102, the motion data analyzer 222 can determine that the user isreaching for the display screen 103 of the user device 102.

The example thermal constraint manager 132 of FIG. 2 includes an imagedata analyzer 220. In this example, the image data analyzer 220 providesmeans for analyzing the sensor data 204 generated by the image sensor(s)122. The image data analyzer 220 uses the gesture data model(s) 223and/or the facial feature data model(s) 225 to analyzes the sensor data204 to identify, for instance, gesture(s) being performed by the userand/or the user's posture relative to the device 102, and/or to track aposition of the user's eyes relative to the device 102. For example,based on the gesture data model(s) 223 and the image sensor data 204,the image data analyzer 220 can determine that the user is typing. Inother examples, based on the facial feature data model(s) 225 and theimage sensor data 204 including a head of the user, the image dataanalyzer 220 determines that the user is turned away from the device 102because the user's eyes are not visible in the image data.

In the example of FIG. 2, the thermal constraint manager 132 includes atimer 244. In this example, the timer 244 provides means for monitoringa duration of time within which a user input is received at the userdevice 102 after the user presence detection analyzer 214 determinesthat the user is within the range of the user presence detectionsensor(s) 118. The timer 244 additionally or alternatively providesmeans for monitoring a duration of time in which the motion dataanalyzer 222 and/or the image data analyzer 220 determine that there isa likelihood of user interaction within the device after the userpresence detection analyzer 214 determines that the user is within therange of the user presence detection sensor(s) 118. The timer 244monitors the amount of time that has passed based on time intervalthreshold(s) 246 stored in the database 212 and defined by userinput(s). As disclosed herein, if a user input is not received withinthe time interval threshold(s) 246 and/or if the motion data analyzer222 and/or the image data analyzer 220 have not determined that a userinteraction with the device 102 is likely to occur within the timeinterval threshold(s) 246, the thermal constraint manager 132 can adjustthe thermal constraint(s) and/or the fan acoustic constraint(s) inresponse to the lack of user interaction with the device 102.

The thermal constraint manager 132 of FIG. 2 includes an ambient noiseanalyzer 234. In this example, the ambient noise analyzer 234 providesmeans for analyzing the sensor data 206 generated by the ambient noisesensor(s) 124. The ambient noise analyzer 234 analyzes the sensor data206 analyzes the sensor data 206 based on one or more ambient noiserule(s) 235. In the example of FIG. 2, the ambient noise rule(s) 235define threshold ambient noise level(s) that, if exceeded, indicate thata user is unlikely to detect an increase in audible fan noise. Theambient noise rule(s) 235 can be defined based on user input(s) andstored in the database 212.

The thermal constraint manager 132 of FIG. 2 includes a temperatureanalyzer 236. In this example, the temperature analyzer 236 providesmeans for analyzing the sensor data 208 generated by the temperaturesensor(s) 126. The temperature analyzer 236 analyzes the sensor data 208to determine the temperature of one or more hardware component(s) of theuser device 102 and/or the skin of the housing of the user device 102.For example, the temperature analyzer 236 can detect an amount of heatgenerated by the processor 130 and/or a temperature of the exterior skinof the housing 102 during operation of the device 102.

The example thermal constraint manager 132 of FIG. 2 includes a sensormanager 248 to manage operation of one or more of the user presencedetection sensor(s) 118, the device configuration sensor(s) 120, theimage sensor(s) 122, the motion sensor(s) 122, the ambient noisesensor(s) 124, and/or the temperature sensor(s) 126. The sensor manager248 controls operation of the sensor(s) 118, 120, 122, 124, 126 based onone or more sensor activation rule(s) 250. The sensor activation rule(s)250 can be defined by user input(s) and stored in the database 212.

In some examples, the sensor activation rule(s) 250 define rule(s) foractivating the sensor(s) to conserve power consumption by the device102. For example, the sensor activation rule(s) 250 can define that theuser presence detection sensor(s) 118 should remain active while thedevice 102 is operative (e.g., in a working power state) and that theimage sensor(s) 122 should be activated when the user presence detectionanalyzer 214 determines that a user is within the range of the userpresence detection sensor(s) 118. Such a rule can prevent unnecessarypower consumption by the device 102 when, for instance, the user is notproximate to the device 102. In other examples, the sensor manager 248selectively activates the image sensor(s) 122 to supplement datagenerated by the motion sensor(s) 123 to increase an accuracy with whichthe gesture(s) of the user are detected. In some examples, the sensormanager 248 deactivates the image sensor(s) 122 if the image dataanalyzer 220 does not predict a likelihood of a user interaction withthe device and/or the device 102 does not receive a user input within atime threshold defined by the timer 244 to conserve power.

The example thermal constraint manager 132 of FIG. 2 includes a thermalconstraint selector 252. In the example of FIG. 2, the thermalconstraint selector 252 selects a thermal constraint to be assigned tothe user device 102 based on one or more of data from the user presencedetection analyzer 214, the device configuration analyzer 218, themotion data analyzer 222, the image data analyzer 220, the ambient noiseanalyzer 234, and/or the temperature analyzer 236. The example thermalconstraint selector 252 selects the thermal constraint to be assigned tothe user device based on one or more thermal constraint selectionrule(s) 254. The thermal constraint selection rule(s) 254 are definedbased on user input(s) and stored in the database 212.

For example, the thermal constraint selection rule(s) 254 can include afirst rule that if the device configuration analyzer 218 determines thatthe user is providing input(s) via a keyboard or touch screen of thedevice 102, a first, or default thermal constraint for the temperatureof the skin of the housing device 102 should be assigned to the userdevice 102 to prevent discomfort to the user when touching the device102. The default thermal constraint for the skin temperature can be for,for example, 45° C. The thermal constraint selection rule(s) 254 caninclude a second rule that if the device configuration analyzer 218determines that the user is providing input(s) via the external keyboard108, a second thermal constraint should be assigned to the device 102,where the second thermal constraint provides for an increased skintemperature of the device as compared to the first (e.g., default)thermal constraint. For example, the second thermal constraint candefine a skin temperature limit of 48° C.

The example thermal constraint manager 132 of FIG. 2 includes a fanacoustic constraint selector 258. In the example of FIG. 2, the fanacoustic constraint selector 258 selects a fan acoustic constraint to beassigned to the user device 102 based on one or more of data from theuser presence detection analyzer 214, the device configuration analyzer218, the motion data analyzer 222, the image data analyzer 220, theambient noise analyzer 234, and/or the temperature analyzer 236. Theexample thermal constraint selector 252 selects the fan acousticconstraint to be assigned to the user device 102 based one or more fanacoustic constraint selection rule(s) 260. The fan acoustic constraintselection rule(s) 260 are defined based on user input(s) and stored inthe database 212.

For example, the fan acoustic constraint selection rule(s) 260 caninclude a first or default rule for the fan noise level based on datafrom the user presence detection analyzer 214 indicating that the useris within a first range of the user presence detection sensor(s) 118(e.g., 0.5 meters from the device 102). The first rule can define asound pressure level corresponding to 35 dBA for noise generated by thefan(s). The fan acoustic constraint selection rule(s) 260 can include asecond rule for the fan noise level based on data from the user presencedetection analyzer 214 indicating that the user is within a second rangeof the user presence detection sensor(s) 118 (e.g., 1 meter from thedevice 102), where the second rule defines a sound pressure levelcorresponding to a sound pressure level (e.g., 41 dBA) for noisegenerated by the fan(s) 114 that is greater than the sound pressurelevel defined by the first rule. The fan acoustic constraint selectionrule(s) 260 can include a third rule for the fan noise level based ondata from the image data analyzer 220 indicating that the user is turnedaway from the user device 102. The third rule can define a fan speedand, thus, acoustic noise level, that is increased relative to the fanspeed and associated acoustic noise defined by the first or default fanacoustic rule in view of the determination that the user is notinteracting or not likely interacting with the device 102. The fanacoustic constraint selection rule(s) 260 can include a fourth ruleindicating that if the device configuration analyzer 218 determines thatan angle of a display screen of the device 102 is within a particularangle range relative to, for instance, a base of a laptop, the user issitting when interacting with the device 102 and, thus, located closerto the device than if the user is standing. In such examples, the fourthrule can define a reduced fan acoustic noise level as compared to if theuser is standing or located farther from the device 102.

The fan acoustic constraint selection rule(s) 260 can include a fifthrule indicating that if the device configuration analyzer 218 thatheadphones are coupled to the device 102 and/or the image data analyzer220 determine that the user is wearing headphones, the fan acousticnoise can be increased relative to the default fan noise level. The fanacoustic constraint selection rule(s) 260 can include a fifth ruleindicating that if the ambient noise analyzer 234 determines that thefan noise exceeds an ambient noise threshold, the fan acoustic noise canbe increased relative to the default fan noise level. The fan acousticconstraint selection rule(s) 260 can include a sixth rule indicatingthat if the device configuration analyzer 218, the image data analyzer220, and/or the motion data analyzer 222 do not detect a user inputand/or a predict a likelihood of a user interaction with the device 102within the time interval threshold(s) 246 as monitored by the timer 244,the fan acoustic noise should be increased because the user is notlikely interacting with the device 102.

In the example of FIG. 2, the thermal constraint selector 252 and thefan acoustic constraint selector 258 can communicate to optimizeperformance of the device 102, thermal constraints for the skin of thedevice 102, and fan acoustic noise levels in view of user interaction(s)and/or ambient conditions. For example, if the device configurationanalyzer 218 determines that user is providing user inputs via anexternal device, the thermal constraint selector 252 can select a firstthermal constraint that results in increased skin temperature of thedevice (e.g., 46° C.) relative to a default temperature (e.g., 45° C.).If the ambient noise analyzer 234 determines that the user device is ina quiet environment, the fan acoustic constraint selector 258 can selecta first fan acoustic constraint for the device 102 that permits for amodest increase in fan noise level(s) (e.g., 38 dBA) over a defaultlevel (e.g., 35 dBA) to accommodate the increased heat permitted by thefirst thermal constraint and prevent overheating of the device 102.However, if the ambient noise analyzer 234 determines that the userdevice 102 is in a loud environment, the thermal constraint selector 252can select a second thermal constraint for the device 102 that providesfor an increased skin temperature (e.g., 48° C.) over the first thermalconstraint (e.g., 46° C.) and the default thermal constraint (e.g., 45°C.) and, thus, permits increased device performance as result ofincreased power consumption by the device component(s). Also, the fanacoustic constraint selector can select a second fan acoustic constraintfor the device 102 that permits an increase in fan noise level(s) (e.g.,41 dBA) over the first fan constraint (e.g., 38 dBA) and the default fanacoustic constraint (e.g., 35 dBA). Because the device 102 is in a loudenvironment, the performance of the device 102 can be increased bypermitting increased heat to be generated by the component(s) of thedevice 102 as compared to if the device 102 where in a quiet environmentand the fan acoustic constraints were limited in view of low ambientnoise.

The thermal constraint manager 132 of FIG. 2 includes a power sourcemanager 238. In this example, the power source manager 238 generatesinstruction(s) that are transmitted to the power source(s) 116 of theuser device 102 of FIG. 1 to control the power provided to the processor130 and/or other hardware components of the user device 102 (e.g., avideo graphics card). As disclosed herein, increasing the power providedto the hardware component(s) of the device 102 increases the performancelevel of those component(s) (e.g., the responsiveness, availability,reliability, recoverability, and/or throughput of the processor 130). Inthe example of FIG. 2, the thermal constraint selector 252 communicateswith the power source manager 238 to increase or decrease the powerprovided to the hardware component(s) of the device 102 in view of theselected thermal constraint(s). For example, if the thermal constraintselector 252 selects a thermal constraint for the device skintemperature that allows the skin temperature to increase relative to adefault skin temperature limit, the power source manager 238 generatesinstructions for increased power to be provided to the hardwarecomponent(s) of the device 102. If the thermal constraint selector 252determines that the skin temperature of the device 102 should be reduced(e.g., in response to a change in user interaction with the device 102),the power source manager 238 generates instructions for power providedto the hardware component(s) of the device 102 to be reduced to decreasethe amount of heat generated by the component(s). The example powersource manager 238 transmits the instruction(s) to the power source 116via one or more wired or wireless connections.

The example thermal constraint manager 132 of FIG. 2 includes a fanspeed manager 240. The fan speed manager 240 generates instruction(s) tocontrol the fan speed (e.g., revolutions per minute) of the fan(s) 114of the user device 102 of FIG. 1 in response to selection of a fanacoustic constraint by the fan acoustic constraint selector 258. In someexamples, the fan speed manager 240 generates instruction(s) to controlspeed of the fan(s) 114 in response to selection of a thermal constraintby the thermal constraint selector 252 to prevent, for instance,overheating of the hardware component(s) of the device when the selectedthermal constraint permits an increase in skin temperature of the device102. The fan speed manager 240 transmits the instruction(s) to thefan(s) 114 via one or more wired or wireless connections.

In some examples, the fan acoustic constraint selector 258 selects a fanacoustic constraint associated with increased fan acoustic noise whenthe user presence detection analyzer 214 does not detect the presence ofa user within the range of the user presence detection sensor(s) 118 orwhen the user presence detection analyzer 214 determines that the useris a predefined distance from the device 102 to facilitate heatsink andfan shroud cleaning of heatsink(s) and fan shroud(s) of the device 102(e.g., to remove accumulated dust). Because heatsink and fan shroudcleaning can increase acoustic generated by the fan(s) 114 when rotationof the fan(s) 114 are reversed to perform the cleaning, the fan acousticconstraint selector 258 can select a fan acoustic constraint for thedevice 102 and communicate with the fan speed manager 240 to perform thecleaning when user(s) are not proximate to the device 102. In suchexamples, the acoustic noise of the fan(s) 114 can be increased withoutdisrupting a user interacting with the device 102 and longevity of thedevice performance can be increased though periodic cleanings.

The example thermal constraint selector 252 of FIGS. 1 and/or 2dynamically selects the thermal constraint to be assigned to the device102 based on analysis of the sensor data. For example, at first time,the thermal constraint selector 252 can select a first thermalconstraint for the device 102 that corresponds to increased temperatureof the skin of the housing of the device 102 based on data indicatingthe user is providing input(s) via the external keyboard 108. If, at alater time, the gesture data analyzer detects that the user is reachingfor the display screen 103 of the device 102, the thermal constraintselector 252 selects a second thermal constraint for the device 102 thatreduces the skin temperature of the device. In response, the powersource manager 238 generates instructions to adjust the power providedto the hardware component(s) of the device to reduce heat generatedand/or the fan speed manager 240 generate instructions to adjust the fanspeed(s) (e.g., increase the fan speed(s) to exhaust hot air) in view ofthe change in the thermal constraint selected for the device 102.

In some examples, the thermal constraint selector 252 and/or the fanacoustic constraint selector 258 selectively adjust the constraint(s)applied to the device 102 based on temperature data generated by thetemperature sensor(s) 126 during operation of the device. For example,if increased power is provided to the hardware component(s) of thedevice 102 in response to selection of a thermal constraint the permitsincreased skin temperature of the housing of the device 102, the fanspeed manager 240 can instruct the fan(s) 114 to increase rotationalspeed to prevent the skin temperature from exceeding the selectedthermal constraint based on data from the temperature sensor(s) 126.

Although the example thermal constraint manager 132 of FIGS. 1 and/or 2is discussed in connection with analysis of sensor data from the userpresence detection sensor(s) 118, the user input sensor(s) 120, theimage sensor(s) 122, and/or the ambient noise sensor(s) 124, the examplethermal constraint manager 132 can analyze data based on other sensorsof the user device 102 of FIG. 1 (e.g., ambient light sensor(s)) toevaluate user interaction(s) and/or the environment in which the device102 is located and assign thermal and/or fan acoustic constraints to thedevice 102.

While an example manner of implementing the thermal constraint manager132 of FIG. 1 is illustrated in FIG. 2, one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example user presence detection analyzer 214, the exampledevice configuration analyzer 218, the example image data analyzer 220,the example motion data analyzer 222, the example ambient noise analyzer234, the example temperature analyzer 236, the example power sourcemanager 238, the example fan speed manager 240, the example timer 244,the example sensor manager 248, the example thermal constraint selector252, the example fan acoustic constraint selector 258, the exampledatabase 212 and/or, more generally, the example thermal constraintmanager 132 of FIG. 2 may be implemented by hardware, software, firmwareand/or any combination of hardware, software and/or firmware. Thus, forexample, any of the example user presence detection analyzer 214, theexample device configuration analyzer 218, the example image dataanalyzer 220, the example motion data analyzer 222, the example ambientnoise analyzer 234, the example temperature analyzer 236, the examplepower source manager 238, the example fan speed manager 240, the exampletimer 244, the example sensor manager 248, the example thermalconstraint selector 252, the example fan acoustic constraint selector258, the example database 212 and/or, more generally, the examplethermal constraint manager 132 could be implemented by one or moreanalog or digital circuit(s), logic circuits, programmable processor(s),programmable controller(s), graphics processing unit(s) (GPU(s)),digital signal processor(s) (DSP(s)), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example userpresence detection analyzer 214, the example device configurationanalyzer 218, the example image data analyzer 220, the example motiondata analyzer 222, the example motion data analyzer 222, the exampleambient noise analyzer 234, the example temperature analyzer 236, theexample power source manager 238, the example fan speed manager 240, theexample timer 244, the example sensor manager 248, the example thermalconstraint selector 252, and/or the example fan acoustic constraintselector 258, the example database 212 is/are hereby expressly definedto include a non-transitory computer readable storage device or storagedisk such as a memory, a digital versatile disk (DVD), a compact disk(CD), a Blu-ray disk, etc. including the software and/or firmware.Further still, the example thermal constraint manager 132 of FIG. 2 mayinclude one or more elements, processes and/or devices in addition to,or instead of, those illustrated in FIG. 2, and/or may include more thanone of any or all of the illustrated elements, processes and devices. Asused herein, the phrase “in communication,” including variationsthereof, encompasses direct communication and/or indirect communicationthrough one or more intermediary components, and does not require directphysical (e.g., wired) communication and/or constant communication, butrather additionally includes selective communication at periodicintervals, scheduled intervals, aperiodic intervals, and/or one-timeevents.

While an example manner of implementing the training manager 224 isillustrated in FIG. 2, one or more of the elements, processes and/ordevices illustrated in FIG. 2 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample trainer 224, the example machine learning engineer 228, theexample database 232 and/or, more generally, the example trainingmanager 224 of FIG. 2 may be implemented by hardware, software, firmwareand/or any combination of hardware, software and/or firmware. Thus, forexample, any of the example trainer 224, the example machine learningengineer 228, the example database 232 and/or, more generally, theexample training manager 224 could be implemented by one or more analogor digital circuit(s), logic circuits, programmable processor(s),programmable controller(s), graphics processing unit(s) (GPU(s)),digital signal processor(s) (DSP(s)), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example trainer 224,the example machine learning engineer 228, and/or the example database232 is/are hereby expressly defined to include a non-transitory computerreadable storage device or storage disk such as a memory, a digitalversatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc.including the software and/or firmware. Further still, the exampletraining manager 224 of FIG. 2 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 2, and/or may include more than one of any or all ofthe illustrated elements, processes and devices. As used herein, thephrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

FIG. 3 illustrates a graph 300 of example thermal constraints that maybe implemented in connection with an electronic user device such as theexample user device 102 of FIG. 1 to control a temperature of anexterior surface, or skin, of the device (e.g., a housing or body of thedevice). In particular, the example graph 300 of FIG. 3 illustratestemperature of the skin of the user device 102 over time for differentthermal constraints. A default temperature for the skin of the device102 can be set at 45° C., as represented by line 302 in FIG. 3. A firstthermal constraint 304 corresponds to a default thermal constraint inthat, when implemented by the device 102, the skin temperature of theuser device 102 does not exceed the default skin temperature representedby line 302. As disclosed herein, in some examples, the thermalconstraint manager 132 of FIGS. 1 and/or 2 determines that a thermalconstraint that permits the skin temperature of the device 102 toincrease can be selected in view of, for instance, user interaction(s)with the device 102. As shown in FIG. 3, a second thermal constraint 306provides for an increase in skin temperature relative to the firstthermal constraint 304 (e.g., a skin temperature limit of 46° C.). Athird thermal constraint 308 and a fourth thermal constraint 310 permitadditional increases in skin temperature relative to the first andsecond thermal constraints 304, 306. If one or more of the second,third, or fourth thermal constraints 306, 308, 310 is selected, thepower source manager 238 of the example thermal constraint manager 132generates instructions to increases the power provided to the hardwarecomponent(s) of the user device 102, which allows the component(s) togenerate more heat without violating the thermal constraint and improveperformance of the device 102.

FIG. 4 illustrates an example user device 400 (e.g., the user device 102of FIG. 1) in which examples disclosed herein may be implemented. InFIG. 4, the example user device 400 is a laptop. However, as disclosedherein, other types of user devices, such as desktops or electronictablets, can be used to implement the examples disclosed herein.

FIG. 4 illustrates the user device 400 in a first configuration in whicha user 402 interacts with the user device 400 by providing input(s) viaan on-board keyboard 404 (e.g., the keyboard 104) of the device 400. Thekeyboard 404 is supported by a housing 406 of the device 400, where thehousing 406 includes an exterior surface or skin 408 that defines thehousing 406. To prevent the temperature of one or more portions of theskin 408 from becoming too hot while the user is directly touching thedevice 400, the example thermal constraint manager 132 of FIGS. 1 and/or2 can select a thermal constraint for the device 400 that maintains theskin temperature at or substantially at a default level (e.g., the firstthermal constraint 304 of FIG. 3 corresponding to a skin temperature of45° C. for the skin 408). In such examples, the power source manager 238of the example thermal constraint manager 132 manages power level(s) forthe hardware component(s) of the device 400 so that the resultingtemperature of the skin 408 does not exceed the thermal constraint.Additionally or alternatively, the thermal constraint manager 132 candetermine the user 402 is not wearing headphones based on data generatedby, for instance, the device configuration sensor(s) 120 and/or theimage data sensor(s) 122 of FIG. 1. Thus, the fan constraint selector258 can select a fan acoustic constraint for the device 400 so that thenoise generated by the fan(s) of the device 400 (e.g., the fan(s) 114)do not exceed, for instance, a default fan noise level of 35 dBA.

FIG. 5 illustrates the example user device 400 of FIG. 4 in a secondconfiguration in which the user 402 is interacting with the user device102 via an external keyboard 500. Thus, because the user 402 isinteracting with the user device 400 via the external keyboard 500, theuser 402 is not directly touching the device 400 (e.g., the skin 408 ofthe device 400). In this example, the thermal constraint selector 252can select a thermal constraint (e.g., the second, third, or fourththermal constraints 306, 308, 310 of FIG. 3) that permits an increase ina temperature of the skin 408 of the device 400 above the defaulttemperature (e.g., above the temperature associated with the firstthermal constraint 304 of FIG. 3). In view of the permitted increase inthe temperature of the skin 404, power to one or more hardwarecomponents of the device 400 and, thus performance of those component(s)can be increased.

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the example training manager 224 ofFIG. 2 is shown in FIG. 6. The machine readable instructions may be oneor more executable programs or portion(s) of an executable program forexecution by a computer processor such as the processor 224 shown in theexample processor platform 800 discussed below in connection with FIG.8. The program may be embodied in software stored on a non-transitorycomputer readable storage medium such as a CD-ROM, a floppy disk, a harddrive, a DVD, a Blu-ray disk, or a memory associated with the processor224, but the entire program and/or parts thereof could alternatively beexecuted by a device other than the processor 224 and/or embodied infirmware or dedicated hardware. Further, although the example program isdescribed with reference to the flowchart illustrated in FIG. 6, manyother methods of implementing the example training manager 224 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, an FPGA, anASIC, a comparator, an operational-amplifier (op-amp), a logic circuit,etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

FIG. 6 is a flowchart of example machine readable instructions that,when executed, implement the example training manager 224 of FIG. 2. Inthe example of FIG. 6, the training manager 224 trains the examplethermal constraint manager 132 of FIGS. 1 and/or 2 using traininggesture data and/or training facial feature data, which is generated forone or more users who may or may not be using the example user device102 of FIG. 1. As discussed herein, the training manager 224 generatesmachine learning models that are used by the thermal constraint manager132 of FIGS. 1 and/or 2 to select thermal constraint(s) for atemperature of a skin of the a user device (e.g., skin 408 of thehousing 406 the user device 102, 400) and/or fan acoustic constraint(s)for noise generated by fan(s) of the user device (e.g., the fan(s) 114of the user device 102) based on user interaction(s) relative to theuser device 102.

The example instructions of FIG. 6 can be executed by one or moreprocessors of, for instance, the user device 102, another user device(e.g., the user device 128), and/or a cloud-based device (e.g., thecloud-based device(s) 129). The instructions of FIG. 6 can be executedin substantially real-time as the training gesture data and/or thetraining facial feature data is received by the training manager 224 orat some time after the training data is received by the training manager224. The training manager 224 can communicate with the thermalconstraint manager 132 via one or more wired or wireless communicationprotocols.

The example trainer 226 of FIG. 2 accesses training gesture data 230and/or training facial feature data 231 (block 600). The traininggesture data 230 and/or training facial feature data 231 can be storedin the database 232. In some examples, the training gesture data 230and/or training facial feature data 231 is generated for one or moreusers of the user device 102. In some examples, the training gesturedata 230 and/or the training facial feature data 231 can be receivedfrom the thermal constraint manager 132 and/or directly from the imagesensor(s) 122 and/or the motion sensor(s) 123 of the example user device102, 400. In some other examples, the training gesture data 230 and/orthe training facial feature data 231 is generated for users who are notthe user(s) of the user device 102.

The example trainer 226 of FIG. 2 identifies user interactions (e.g.,user interactions with the user device 102, 400 and/or other userinteractions such as talking on a phone) represented by the traininggesture data 230 and/or the training facial feature data 231 (block602). As an example, based on the training gesture data 230, the trainer226 identifies an arm motion in which a user reaches his or her armforward as indicating that the user intends to touch a touch screen of auser device. As another example, based on the training facial featuredata 231, the trainer 226 identifies eye positions indicating that auser is looking toward or away from a display screen of the device.

The example trainer 226 of FIG. 2 generates one or more gesture datamodel(s) 223 via the machine learning engine 228 and based on thetraining gesture data 230 and one or more facial feature data model(s)225 via the machine learning engine 228 and based on the training facialfeature data 231 (block 604). For example, the trainer 2226 uses thetraining gesture data 230 to generate the gesture data model(s) 223 thatare used by the thermal constraint manager 132 to determine whether auser is typing on the keyboard 104, 404 of the user device 102, 400.

The example trainer 226 can continue to train the thermal constraintmanager 132 using different datasets and/or datasets having differentlevels of specificity (block 606). For example, the trainer 226 cangenerate a first gesture data model 223 to determine if the user isinteracting with the keyboard 104 of the user device 102, 400 and asecond gesture data model 223 to determine if the user is interactingwith the pointing device(s) 106 of the user device 102, 400. The exampleinstructions end when there is no additional training to be performed(e.g., based on user input(s)) (block 608).

The example instructions of FIG. 6 can be used to perform training basedon other types of sensor data. For example, the example instructions ofFIG. 6 can be used to train the thermal constraint manager 132 toassociate different orientations of the device 102, 400, screen angle,etc., with different user positions (e.g., sitting, standing) relativeto the device 102, 400 and/or different locations of the device (e.g.,resting a user's lap, held in a user's hand, resting on table).

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the thermal constraint manager 132of FIG. 2 is shown in FIGS. 7A-7B. The machine readable instructions maybe one or more executable programs or portion(s) of an executableprogram for execution by a computer processor such as the processor 132shown in the example processor platform 900 discussed below inconnection with FIG. 9. The program may be embodied in software storedon a non-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associatedwith the processor 132, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor 132and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIGS. 7A-7B, many other methods of implementing the example thermalconstraint manager 132 may alternatively be used. For example, the orderof execution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, or combined. Additionally oralternatively, any or all of the blocks may be implemented by one ormore hardware circuits (e.g., discrete and/or integrated analog and/ordigital circuitry, an FPGA, an ASIC, a comparator, anoperational-amplifier (op-amp), a logic circuit, etc.) structured toperform the corresponding operation without executing software orfirmware.

FIGS. 7A and 7B are flowcharts of example machine readable instructionsthat, when executed, implement the example thermal constraint manager132 of FIGS. 1 and/or 2. In the example of FIGS. 7A and 7B, the thermalconstraint manager 132 generates instruction(s) to control the thermalconstraint(s) and/or fan acoustic constraint(s) of a user device (e.g.,the user device 102, 400) based on a user interaction(s) and/or ambientcondition(s) for an environment in which the device is located. Theexample instructions of FIGS. 7A and 7B can be executed by one or moreprocessors of, for instance, the user device 102, 400 another userdevice (e.g., the user device 128), and/or a cloud-based device (e.g.,the cloud-based device(s) 129). The instructions of FIGS. 7A and 7B canbe executed in substantially real-time as sensor data received by thethermal constraint manager 132 or at some time after the sensor data isreceived by the thermal constraint manager 132.

In the example instructions of FIGS. 7A and 7B, the device 102, 400 canbe in a working power state (e.g., a power state in which the device isfully operational in that the display screen is turned on, applicationsare being executed by processor(s) of the device) or a connected standbystate (e.g., a low power standby state in which the device remainsconnected to the Internet such that processor(s) of the device canrespond quickly to hardware and/or network events).

The example user presence detection analyzer 214 determines whether theuser is within a threshold distance of the user device 102 (block 700).For example, the user presence detection analyzer 214 detects a user isapproaching the user device 102, 400 based on data generated by the userpresence detection sensor(s) 118 (e.g., TOF data, etc.) indicating thatthe user is within the range of the user presence detection sensor(s)118. In some examples, the user presence detection analyzer 214determines if the user is within a predefined distance of the device 102(e.g., within 1 meter, within 0.5 meters, etc.).

In the example of FIGS. 7A and 7B, if the user presence detectionanalyzer 214 of the example thermal constraint manager 132 of FIG. 2determines a user is detected within a threshold distance of the userdevice 102, the example device configuration analyzer 218 of the examplethermal constraint manager 132 of FIG. 2 determines whether userinput(s) are detected within a threshold time (block 702). For example,the timer 244 communicates with the device configuration analyzer 218 todetermine the amount of time between which a user presence is detectedwithin a threshold distance of the device 102, 400 (e.g., block 702) andwhen user input(s) are received by the device 102, 400. In someexamples, the device configuration analyzer 218 detects user input(s) atthe user device 102 such as keyboard input(s), touch screen input(s),mouse click(s), etc. If the device configuration analyzer 218 determinesthe user input(s) are detected within the threshold time, controlproceeds to block 704.

At block 704, the device configuration analyzer 218 determines whetherthe user input(s) are received via external user input device(s) oron-board input device(s). For example, the device configuration analyzer218 detects user input(s) via the external keyboard 108 and/or theexternal pointing device(s) 110 or via the on-board keyboard 104 and/orthe on-board pointing device(s) 106.

If the device configuration analyzer 218 determines that the userinput(s) are received via an external user input device, the thermalconstraint selector 252 of the example thermal constraint manager 132 ofFIG. 2 selects a thermal constraint for a temperature of the skin 408 ofthe device 102 (e.g., based on the thermal constraint selection rule(s)254 stored in the database 212) that permits an increase in atemperature of a skin 408 of a housing 406 of the device 102, 400relative to a default temperature. In response, the power source manager238 of the example thermal constraint manager 132 of FIG. 2 instructsthe hardware component(s) of the device 102, 400 (e.g., the processor130) to consume increased amounts of power (block 706). For example, ifthe device configuration analyzer 218 determines that the user isinteracting with the device 102, 400 via an external keyboard 104, 500,the thermal constraint selector 252 can select a thermal constraint thatpermits the skin temperature to increase to, for instance 47° C. from adefault temperature of 45° C. The power source manager 238 communicateswith the power source(s) 116 of the device 102, 400 to increase thepower provided to the hardware component(s) of the user device 102, 400based on the thermal constraint selected by the thermal constraintselector 252.

If the device configuration analyzer 218 determines that the userinput(s) are being received by the device 102, 400 via on-board userinput device(s) such as the on-board keyboard 104, the thermalconstraint selector 252 of the example thermal constraint manager 132 ofFIG. 2 selects a thermal constraint for a temperature of the skin 408 ofthe device 102 that maintains the temperature of the skin 408 of thehousing 406 of the device 102, 400 at a default temperature and thepower source manager 238 of the example thermal constraint manager 132of FIG. 2 instructs the hardware component(s) of the device 102, 400(e.g., the processor 130) to consume power so as not to cause thetemperature of the skin to exceed the default temperature (block 708).

In some examples, in view of the thermal constraint(s) assigned to thedevice 102, 400 at blocks 706, 708, the temperature analyzer 236monitors the temperature of the hardware component(s) of the user device102 based on the data generated by the temperature sensor(s) 126 and thefan speed manager 240 controls operation of the fan(s) 114 (e.g.,increase fan level to exhaust hot air to cool the user device 102) toprevent the skin temperature from exceeding the selected thermalconstraint at blocks 706 and/or 708.

Control proceeds to block 718 from blocks 706, 708. At block 718, thedevice configuration analyzer 218 determines whether the user who isinteracting with the device 102, 400 is wearing headphones 112. Forexample, the device configuration analyzer 218 detects whetherheadphones 112 are coupled with the user device 102 (e.g., via wired orwireless connection(s)) and audio output(s) are being provided via thedevice 102, 400. In some examples, the image data analyzer 220determines whether the user is wearing headphones 112 based on imagedata generated by the image sensor(s) 122. If the device configurationanalyzer 218 and/or the image data analyzer 220 determine the user iswearing headphones 112, the fan constraint selector 258 selects a fanacoustic constraint that permits the fan(s) 114 to rotate at increasedspeeds and, thus, generate more noise (e.g., 36 dBA) in view of the useof headphones 112 by the user and the fan speed manager 240 instructsthe fan(s) to increase rotational speed(s) (block 720). If the deviceconfiguration analyzer 218 and/or the image data analyzer 220 determinethe user is not wearing headphones, control proceeds to block 724.

At block 724, the ambient noise analyzer 234 analyzes microphone datagenerated by the microphone(s) 124 to determine an ambient noise levelfor an environment in which the user device 102, 400 is located. Theambient noise analyzer 234 determines whether the ambient noise levelexceeds a threshold (e.g., based on the ambient noise rule(s) 235)(block 726). If the ambient noise level exceeds the threshold, the fanconstraint selector 258 selects a fan acoustic constraint that permitsthe fan(s) 114 to rotate at increased speeds and, thus, generate morenoise in view of the noisy surrounding environment and the fan speedmanager 240 instructs the fan(s) to increase rotational speed(s) (block728). If the ambient noise level does not exceed the threshold, the fanacoustic constraint selector 258 selects a default fan acousticconstraint (e.g., based on the fan acoustic constraint selection rule(s)260) for the fan(s) 114 and the fan speed manager 240 of the examplethermal constraint manager 132 of FIG. 1 instructs the fan(s) to rotateat speed(s) that generate noise at or under, for instance 35 dBA (block730). Control returns to block 722.

In the examples of FIGS. 7A and 7B, if the device configuration analyzer218 does not detect the user input(s) within a threshold time (block702), the image data analyzer 220 and/or the motion data analyzer 222analyze user gesture(s) (e.g., movements, posture) and/or facialfeature(s) (e.g., eye gaze) based on data generated by the imagesensor(s) 122 and/or the motion sensor(s) 123 (block 710). In someinstances, the sensor manager 248 activates the image sensor(s) 122 togenerate image data when the user is detected as being proximate to thedevice (block 700).

For example, the image data analyzer 220 analyzes image data generatedby the image sensor(s) 122 to detect, for instance, a user's postureand/or eye gaze direction. Additionally or alternatively, the motiondata analyzer 222 can analyze gesture data generated by the motionsensor(s) 123 to determine user gesture(s) (e.g., raising an arm,reaching a hand away from the user's body). In the example of FIGS. 7Aand 7B, the image data analyzer 220 and/or the motion data analyzer 222use machine-learning based model(s) 223, 225 to determine in a user islikely to interact with the user device 102. If the image data analyzer220 and/or the motion data analyzer 222 determines that the user islikely to interact with the device 102, 400 within a threshold time asmeasured by the timer 244 (block 712), the fan acoustic constraintselector 258 selects a default fan acoustic constraint (e.g., based onthe fan acoustic constraint selection rule(s) 260) for the fan(s) 114 ofthe device 102, 400 (block 714). Based on the default fan acousticconstraint, the fan speed manager 240 of the example thermal constraintmanager 132 of FIG. 1 instructs the fan(s) to rotate at speed(s) thatgenerate noise at or under, for instance 35 dBA. In some examples, atblock 712, the thermal constraint selector 252 selects a default thermalconstraint for the skin temperature of the device 102, 400 so the skinof the device 102, 400 does not exceed a temperature of, for instance,45° C. in anticipation of the user interacting with the device.Thereafter, control returns to block 702 to detect if user input(s) havebeen received at the device 102, 400.

If the image data analyzer 220 and/or the motion data analyzer 222determines the user is not likely to interact with the user device 102within the threshold time, the fan constraint selector 258 selects a fanacoustic constraint that permits the fan(s) 114 to rotate at increasedspeeds and, thus, generate more noise to more efficiently cool thedevice 102, 400 (e.g., while the device 102, 400 is in a working powerstate) and/or to clean the fan(s) 114 (block 716). Also, if the userpresence detection analyzer 214 does not detect the presence of a userwithin the range of sensor(s) 118 (block 700), the fan constraintselector 258 selects a fan acoustic constraint that permits the fan(s)114 to rotate at increased speeds and, thus, generate more noise to moreefficiently cool the device 102, 400 (e.g., while the device 102, 400 isin a working power state) and/or to clean the fan(s) 114. Controlproceeds to block 722.

At block 722, one or more of the user presence detection analyzer 214,the device configuration analyzer 218, the image data analyzer 220,and/or the motion data analyzer 222 determines whether there is a changein user interaction with the user device 102 and/or a change in alikelihood that the user will interact with the user device 102 (block722). For example, the user presence detection analyzer 214 can detectwhether a user is no longer present based on the data generated by theuser presence detection sensor(s) 118. In some other examples, themotion data analyzer 222 detects a user is reaching for the pointingdevice(s) 106 based on the data generated by the motion sensor(s) 123and the gesture data model(s) 223 after a period of time in which theuser was not interacting with the device 102, 400. If the one or more ofthe user presence detection analyzer 214, the device configurationanalyzer 218, the image data analyzer 220, and/or the motion dataanalyzer 222 detect a change in user interaction with the user device102 and/or a change in a likelihood of a user interaction with the userdevice 102, control returns to block 710 to analyzer user behaviorrelative to the device 102. If no change in user interaction with thedevice 102 and/or likelihood of user interaction is detected, controlproceeds to block 734.

The example instructions of FIGS. 7A and 7B continue until the userdevice 102 enters a sleep mode (block 734), at which time the fan speedmanager 240 disables the fan(s) 114 (block 736). If the device 102, 114returns a working power state (or, in some examples, a connected standbystate) (block 738), the example instructions of FIGS. 7A and 7B resumewith detecting presence of the user proximate to the device 102, 400(and moving component(s) such as the processor 130 and fan(s) 114 tohigher power state) (block 700). The example instructions end when thedevice 102, 400 is powered off (blocks 740, 742).

The machine readable instructions described herein in connection withFIG. 6 and/or 7A-7B may be stored in one or more of a compressed format,an encrypted format, a fragmented format, a compiled format, anexecutable format, a packaged format, etc. Machine readable instructionsas described herein may be stored as data (e.g., portions ofinstructions, code, representations of code, etc.) that may be utilizedto create, manufacture, and/or produce machine executable instructions.For example, the machine readable instructions may be fragmented andstored on one or more storage devices and/or computing devices (e.g.,servers). The machine readable instructions may require one or more ofinstallation, modification, adaptation, updating, combining,supplementing, configuring, decryption, decompression, unpacking,distribution, reassignment, compilation, etc. in order to make themdirectly readable, interpretable, and/or executable by a computingdevice and/or other machine. For example, the machine readableinstructions may be stored in multiple parts, which are individuallycompressed, encrypted, and stored on separate computing devices, whereinthe parts when decrypted, decompressed, and combined form a set ofexecutable instructions that implement a program such as that describedherein.

In another example, the machine readable instructions may be stored in astate in which they may be read by a computer, but require addition of alibrary (e.g., a dynamic link library (DLL)), a software development kit(SDK), an application programming interface (API), etc. in order toexecute the instructions on a particular computing device or otherdevice. In another example, the machine readable instructions may needto be configured (e.g., settings stored, data input, network addressesrecorded, etc.) before the machine readable instructions and/or thecorresponding program(s) can be executed in whole or in part. Thus, thedisclosed machine readable instructions and/or corresponding program(s)are intended to encompass such machine readable instructions and/orprogram(s) regardless of the particular format or state of the machinereadable instructions and/or program(s) when stored or otherwise at restor in transit.

The machine readable instructions described herein can be represented byany past, present, or future instruction language, scripting language,programming language, etc. For example, the machine readableinstructions may be represented using any of the following languages: C,C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language(HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example processes of FIGS. 6 and/or 7A-7B may beimplemented using executable instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

As used herein, singular references (e.g., “a”, “an”, “first”, “second”,etc.) do not exclude a plurality. The term “a” or “an” entity, as usedherein, refers to one or more of that entity. The terms “a” (or “an”),“one or more”, and “at least one” can be used interchangeably herein.Furthermore, although individually listed, a plurality of means,elements or method actions may be implemented by, e.g., a single unit orprocessor. Additionally, although individual features may be included indifferent examples or claims, these may possibly be combined, and theinclusion in different examples or claims does not imply that acombination of features is not feasible and/or advantageous.

FIG. 8 is a block diagram of an example processor platform 800structured to execute the instructions of FIG. 6 to implement thetraining manager 224 of FIG. 2. The processor platform 800 can be, forexample, a server, a personal computer, a workstation, a self-learningmachine (e.g., a neural network), a mobile device (e.g., a cell phone, asmart phone, a tablet such as an iPad′), a personal digital assistant(PDA), an Internet appliance, a headset or other wearable device, or anyother type of computing device.

The processor platform 800 of the illustrated example includes aprocessor 224. The processor 224 of the illustrated example is hardware.For example, the processor 224 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example trainer 226 and theexample machine learning engine 228.

The processor 224 of the illustrated example includes a local memory 813(e.g., a cache). The processor 224 of the illustrated example is incommunication with a main memory including a volatile memory 814 and anon-volatile memory 816 via a bus 818. The volatile memory 814 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 816 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 814, 816is controlled by a memory controller.

The processor platform 800 of the illustrated example also includes aninterface circuit 820. The interface circuit 820 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 822 are connectedto the interface circuit 820. The input device(s) 822 permit(s) a userto enter data and/or commands into the processor 224. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 824 are also connected to the interfacecircuit 820 of the illustrated example. The output devices 824 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 820 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 820 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 826. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 800 of the illustrated example also includes oneor more mass storage devices 828 for storing software and/or data.Examples of such mass storage devices 828 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 832 of FIG. 6 may be stored in themass storage device 828, in the volatile memory 814, in the non-volatilememory 816, and/or on a removable non-transitory computer readablestorage medium such as a CD or DVD.

FIG. 9 is a block diagram of an example processor platform 900structured to execute the instructions of FIGS. 7A and 7B to implementthe thermal constraint manager 132 of FIGS. 1 and/or 2. The processorplatform 900 can be, for example, a server, a personal computer, aworkstation, a self-learning machine (e.g., a neural network), a mobiledevice (e.g., a cell phone, a smart phone, a tablet such as an iPad′), apersonal digital assistant (PDA), an Internet appliance, a headset orother wearable device, or any other type of computing device.

The processor platform 900 of the illustrated example includes aprocessor 132. The processor 132 of the illustrated example is hardware.For example, the processor 132 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example user presencedetection analyzer 214, the example device configuration analyzer 218,the example image data analyzer 220, the example motion data analyzer222, the example ambient noise analyzer 234, the example temperatureanalyzer 236, the example power source manager 238, the example fanspeed manager 240, the example timer 244, the example sensor manager248, the example thermal constraint selector 252, and the example fanacoustic constraint selector 258.

The processor 132 of the illustrated example includes a local memory 913(e.g., a cache). The processor 132 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 916 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 914, 916is controlled by a memory controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and/or commands into the processor 132. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 920 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 926. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 932 of FIGS. 7A and 7B may be storedin the mass storage device 928, in the volatile memory 814, in thenon-volatile memory 916, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that providefor dynamic control of thermal constraints and/or fan acousticconstraints of an electronic user device (e.g., a laptop, a tablet).Examples disclosed herein analyze sensor data indicative of, forinstance, user interaction(s) with the device, other user activities(e.g., talking on a phone), and ambient noise to determine if atemperature of a skin of the device can be increased and/or if audiblenoises associated with rotation of the fan(s) of the device can beincreased. Examples disclosed herein detect opportunities for increasedskin temperature (e.g., when a user is interacting with the device viaan external keyboard) and/or increased fan noise (e.g., when a user islocated a threshold distance from the device or in a noisy environment).By permitting the skin temperature of the device to increase, exampledisclosed herein enable increased power to be provided to the hardwarecomponent(s) of the device and, thus, can improve performance (e.g.,processing performance) of the device. By allowing the fan(s) to rotateat increased speed(s) and, thus, generate more noise, examples disclosedherein provide for efficient cooling of the device. The disclosedmethods, apparatus and articles of manufacture improve the efficiency ofusing a computing device by selectively managing thermal constraint(s)for the device to optimize device performance and cooling in view userinteractions with the device and/or ambient conditions. The disclosedmethods, apparatus and articles of manufacture are accordingly directedto one or more improvement(s) in the functioning of a computer.

Example methods, apparatus, systems, and articles of manufacture toimplement thermal management of electronic user devices are disclosedherein. Further examples and combinations thereof include the following:

Example 1 includes an electronic device including a housing, a fan, afirst sensor, a second sensor, and a processor to at least one ofanalyze first sensor data generated by the first sensor to detect apresence of a subject proximate to the electronic device or analyzesecond sensor data generated by the second sensor to detect a gesture ofthe subject, and adjust one or more of an acoustic noise level generatedthe fan or a temperature of an exterior surface of the housing based onone or more of the presence of the subject or the gesture.

Example 2 includes the electronic device of example 1, wherein thesecond sensor includes a camera.

Example 3 includes the electronic device of examples 1 or 2, wherein theprocessor is to adjust the acoustic noise level by generating aninstruction to increase a rotational speed of the fan.

Example 4 includes the electronic device of any of examples 1-3, whereinthe processor is to adjust the temperature of the exterior surface ofthe device by controlling a power source of the device.

Example 5 includes the electronic device of any of examples 1-4, furtherincluding a microphone, the processor to analyze third sensor datagenerated by the microphone to detect ambient noise in an environmentincluding the device, and adjust the acoustic noise level of the fanbased on the ambient noise.

Example 6 includes the electronic device of example 1, further includinga keyboard carried by the housing, wherein the processor is to detect aninput via the keyboard and adjust the temperature of the exteriorsurface of the housing based on the detection of the input.

Example 7 includes the electronic device of example 1, further includinga keyboard external to the housing, wherein the processor is to detectan input via the keyboard and adjust the temperature of the exteriorsurface of the housing based on the detection of the input.

Example 8 includes the electronic device of example 1, wherein theprocessor is to adjust one the acoustic noise level to during cleaningof the fan and based on the distance of the user being within athreshold distance from the electronic device.

Example 9 includes an apparatus including a user presence detectionanalyzer, an image data analyzer, a motion data analyzer, at least oneof (a) the user presence detection analyzer to identify a presence of auser relative to an electronic device based on first sensor datagenerated by a first sensor of the electronic device or (b) at least oneof the image data analyzer or the motion data analyzer to determine agesture of the user relative to the device based on second sensor datagenerated by a second sensor of the electronic device, a thermalconstraint selector to select a thermal constraint for a temperature ofan exterior surface of the electronic device based on one or more of thepresence of the user or the gesture, and a power source manager toadjust a power level for a processor of the electronic device based onthe thermal constraint.

Example 10 includes the apparatus of example 9, further including adevice configuration analyzer to detect a presence of an external userinput device communicatively coupled to the electronic device.

Example 11 includes the apparatus of example 10, wherein the externaldevice is at least one of a keyboard, a pointing device, or headphones.

Example 12 includes the apparatus of example 9, wherein the secondsensor data is image data and the image data analyzer is to determinethe gesture based on a machine learning model.

Example 13 includes the apparatus of examples 9 or 12, wherein thesecond sensor data is image data and wherein the image data analyzer isto detect a position of an eye of the user relative to a display screenof the electronic device.

Example 14 includes the apparatus of example 9, further including a fanacoustic constraint selector to select a fan acoustic constraint for anoise level to be generated by a fan of the electronic device duringoperation of the fan.

Example 15 includes the apparatus of example 14, further including anambient noise analyzer to determine an ambient noise level based onambient noise data generated by a microphone of the electronic device,the fan acoustic constraint selector to select the fan acousticconstraint based on the ambient noise level.

Example 16 includes the apparatus of example 14, wherein the userpresence detection sensor is further to determine a distance of the userfrom the electronic device, the fan acoustic constraint selector toselect the fan acoustic constraint based on the distance.

Example 17 includes the apparatus of example 14, wherein the fanacoustic constraint selector is to select the fan acoustic constraintfor the noise level to be generated by the fan during cleaning of thefan.

Example 18 includes the apparatus of example 14, wherein the image dataanalyzer is to detect that the user is wearing headphones based on imagedata generated by the second sensor, the fan acoustic constraintselector to select the fan acoustic constraint based on the ambientnoise level based on the detection of the headphones.

Example 19 includes at least one non-transitory computer readablestorage medium including instructions that, when executed, cause amachine to at least identify one or more of (a) a presence of a userrelative to an electronic device based on first sensor data generated bya first sensor of the electronic device, (b) a facial feature of theuser based on second sensor data generated by a second sensor of theelectronic device, or (c) a gesture of the user based on the secondsensor data, select a thermal constraint for a temperature of anexterior surface of the electronic device based on one or more of thepresence of the user, the facial feature, or the gesture, and adjust apower level for a processor of the electronic device based on thethermal constraint.

Example 20 includes the at least one non-transitory computer readablestorage medium of example 19, wherein the instructions, when executed,further cause the machine to detect a presence of an external user inputdevice communicatively coupled to the electronic device.

Example 21 includes the at least one non-transitory computer readablestorage medium of example 19, wherein the instructions, when executed,further cause the machine to identify the gesture based on a machinelearning model.

Example 22 includes the at least one non-transitory computer readablestorage medium of examples 19 or 21, wherein the facial feature includesan eye position and wherein the instructions, when executed, furthercause the machine to detect a position of an eye of the user relative toa display screen of the electronic device.

Example 23 includes the at least one non-transitory computer readablestorage medium of examples 19 or 20, wherein the instructions, whenexecuted, further cause the machine to select a fan acoustic constraintfor a noise level to be generated by a fan of the electronic deviceduring operation of the fan.

Example 24 includes the at least one non-transitory computer readablestorage medium of example 23, wherein the instructions, when executed,further cause the machine to determine an ambient noise level based onambient noise data generated by a microphone of the electronic device,the fan acoustic constraint selector to select the fan acousticconstraint based on the ambient noise level.

Example 25 includes the at least one non-transitory computer readablestorage medium of example 23, wherein the instructions, when executed,further cause the machine to detect that the user is wearing headphonesbased on image data generated by the second sensor, the fan acousticconstraint selector to select the fan acoustic constraint based on thedetection of the headphones.

Example 26 includes the at least one non-transitory computer readablestorage medium of example 23, wherein the instructions, when executed,further cause the machine to determine a distance of the user from theelectronic device and select the fan acoustic constraint based on thedistance.

Example 27 includes the at least one non-transitory computer readablestorage medium of example 23, wherein the instructions, when executed,further cause the machine to select the fan acoustic constraint for thenoise level to be generated by the fan during cleaning of the fan.

Example 28 includes a method including at least one of (a) identifying apresence of a user relative to an electronic device based on firstsensor data generated by a first sensor of the electronic device, (b)identifying a facial feature of the user based on second sensor datagenerated by a second sensor of the electronic device, or (c)identifying a gesture of the user based on the second sensor data,selecting a thermal constraint for a temperature of an exterior surfaceof the electronic device based on one or more of the presence of theuser, the facial feature, or the gesture, and adjusting a power levelfor a processor of the electronic device based on the thermalconstraint.

Example 29 includes the method of example 28, further includingdetecting a presence of an external user input device communicativelycoupled to the electronic device.

Example 30 includes the method of example 28, further includingdetermining the one or more of the facial feature or the gesture basedon a machine learning model.

Example 31 includes the method of examples 28 or 30, wherein the facialfeature includes eye position and further including detecting a positionof an eye of the user relative to a display screen of the electronicdevice.

Example 32 includes the method of examples 28 or 29, further includingselecting a fan acoustic constraint for a noise level to be generated bya fan of the electronic device.

Example 33 includes the method of example 32, further includingdetermining an ambient noise level based on ambient noise data generatedby a microphone of the electronic device, the fan acoustic constraintselector to select the fan acoustic constraint based on the ambientnoise level.

Example 34 includes the method of example 32, further includingdetecting detect that the user is wearing headphones based on image datagenerated by the second sensor, the fan acoustic constraint selector toselect the fan acoustic constraint based on the detection of theheadphones.

Example 35 includes the method of example 32, further includingdetermining a distance of the user from the electronic device andselecting the fan acoustic constraint based on the distance.

Example 36 includes the method of example 32, further includingselecting the fan acoustic constraint for the noise level to begenerated by the fan during cleaning of the fan.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

The following claims are hereby incorporated into this DetailedDescription by this reference, with each claim standing on its own as aseparate embodiment of the present disclosure.

1. An electronic device comprising: a housing; a fan; a first sensor; asecond sensor; and a processor to: at least one of analyze first sensordata generated by the first sensor to detect a presence of a subjectproximate to the electronic device or analyze second sensor datagenerated by the second sensor to detect a gesture of the subject; andadjust one or more of an acoustic noise level generated the fan or atemperature of an exterior surface of the housing based on one or moreof the presence of the subject or the gesture.
 2. (canceled)
 3. Theelectronic device of claim 1, wherein the processor is to adjust theacoustic noise level by generating an instruction to increase arotational speed of the fan.
 4. The electronic device of claim 1,wherein the processor is to adjust the temperature of the exteriorsurface of the device by controlling a power source of the device. 5.The electronic device of claim 1, further including a microphone, theprocessor to: analyze third sensor data generated by the microphone todetect ambient noise in an environment including the device; and adjustthe acoustic noise level of the fan based on the ambient noise.
 6. Theelectronic device of claim 1, further including a keyboard carried bythe housing, wherein the processor is to detect an input via thekeyboard and adjust the temperature of the exterior surface of thehousing based on the detection of the input.
 7. The electronic device ofclaim 1, further including a keyboard external to the housing, whereinthe processor is to detect an input via the keyboard and adjust thetemperature of the exterior surface of the housing based on thedetection of the input.
 8. The electronic device of claim 1, wherein theprocessor is to adjust one the acoustic noise level to during cleaningof the fan and based on the distance of the user being within athreshold distance from the electronic device.
 9. An apparatuscomprising: a user presence detection analyzer; an image data analyzer;a motion data analyzer, at least one of (a) the user presence detectionanalyzer to identify a presence of a user relative to an electronicdevice based on first sensor data generated by a first sensor of theelectronic device or (b) at least one of the image data analyzer or themotion data analyzer to determine a gesture of the user relative to thedevice based on second sensor data generated by a second sensor of theelectronic device; a thermal constraint selector to select a thermalconstraint for a temperature of an exterior surface of the electronicdevice based on one or more of the presence of the user or the gesture;and a power source manager to adjust a power level for a processor ofthe electronic device based on the thermal constraint.
 10. The apparatusof claim 9, further including a device configuration analyzer to detecta presence of an external user input device communicatively coupled tothe electronic device.
 11. (canceled)
 12. (canceled)
 13. The apparatusof claim 9, wherein the second sensor data is image data and wherein theimage data analyzer is to detect a position of an eye of the userrelative to a display screen of the electronic device.
 14. The apparatusof claim 9, further including a fan acoustic constraint selector toselect a fan acoustic constraint for a noise level to be generated by afan of the electronic device during operation of the fan.
 15. (canceled)16. The apparatus of claim 14, wherein the user presence detectionsensor is further to determine a distance of the user from theelectronic device, the fan acoustic constraint selector to select thefan acoustic constraint based on the distance.
 17. The apparatus ofclaim 14, wherein the fan acoustic constraint selector is to select thefan acoustic constraint for the noise level to be generated by the fanduring cleaning of the fan.
 18. The apparatus of claim 14, wherein theimage data analyzer is to detect that the user is wearing headphonesbased on image data generated by the second sensor, the fan acousticconstraint selector to select the fan acoustic constraint based on theambient noise level based on the detection of the headphones.
 19. Atleast one non-transitory computer readable storage medium comprisinginstructions that, when executed, cause a machine to at least: identifyone or more of (a) a presence of a user relative to an electronic devicebased on first sensor data generated by a first sensor of the electronicdevice, (b) a facial feature of the user based on second sensor datagenerated by a second sensor of the electronic device, or (c) a gestureof the user based on the second sensor data; select a thermal constraintfor a temperature of an exterior surface of the electronic device basedon one or more of the presence of the user, the facial feature, or thegesture; and adjust a power level for a processor of the electronicdevice based on the thermal constraint.
 20. The at least onenon-transitory computer readable storage medium of claim 19, wherein theinstructions, when executed, further cause the machine to detect apresence of an external user input device communicatively coupled to theelectronic device.
 21. (canceled)
 22. (canceled)
 23. The at least onenon-transitory computer readable storage medium of claim 19, wherein theinstructions, when executed, further cause the machine to select a fanacoustic constraint for a noise level to be generated by a fan of theelectronic device during operation of the fan.
 24. The at least onenon-transitory computer readable storage medium of claim 23, wherein theinstructions, when executed, further cause the machine to determine anambient noise level based on ambient noise data generated by amicrophone of the electronic device, the fan acoustic constraintselector to select the fan acoustic constraint based on the ambientnoise level.
 25. (canceled)
 26. The at least one non-transitory computerreadable storage of claim 23, wherein the instructions, when executed,further cause the machine to determine a distance of the user from theelectronic device and select the fan acoustic constraint based on thedistance.
 27. The at least one non-transitory computer readable storageof claim 23, wherein the instructions, when executed, further cause themachine to select the fan acoustic constraint for the noise level to begenerated by the fan during cleaning of the fan. 28.-36. (canceled)